At the end of this session, you will have learned how to:
dplyr module to manipulate RxXdfData data objectsRxXdfData objects quickly and easilydplyrXdf package and when to use functions from the RevoScaleR packageMicrosoft R Family
Microsoft R Family
RevoScaleR package enables R users to manipulate data that is larger than memoryxdf (short for eXternal Data Frame), which are highly efficient out-of-memory objectsRevoScaleR functions have a dramatically different syntax from base R functionsdplyr package is an exceptionally popular, due to its appealing syntax, and it’s extensibilitydplyrXdf that exposes most of the dplyr functionality to xdf objectsdplyrXdf abstracts this task of file management, so that you can focus on the data itself, rather than the management of intermediate filesdplyr, or other base R packages, dplyrXdf allows you to work with data residing outside of memory, and therefore scales to datasets of arbitrary sizedplyr trainingdevtools)[github.com/hadley/devtools] (and if on a Windows machine, Rtools)your_name <- "alizaidi"
your_dir <- paste0('/datadrive/', your_name)
# File Path to your Data
your_data <- file.path(your_dir, 'tripdata_2015.xdf')
dir.create(your_dir)
## Warning in dir.create(your_dir): '/datadrive/alizaidi' already exists
download.file("http://alizaidi.blob.core.windows.net/training/yellow_tripdata_2015.xdf",
destfile = your_data)
library(dplyrXdf)
taxi_xdf <- RxXdfData(your_data)
taxi_xdf %>% head
## VendorID passenger_count trip_distance RateCodeID store_and_fwd_flag
## 1 2 1 1.59 1 N
## 2 1 1 3.30 1 N
## 3 1 1 1.80 1 N
## 4 1 1 0.50 1 N
## 5 1 1 3.00 1 N
## 6 1 1 9.00 1 N
## payment_type fare_amount tip_amount tolls_amount pickup_hour pickup_dow
## 1 1 12.0 3.25 0.00 6-10 Thu
## 2 1 14.5 2.00 0.00 6-10 Sat
## 3 2 9.5 0.00 0.00 6-10 Sat
## 4 2 3.5 0.00 0.00 6-10 Sat
## 5 2 15.0 0.00 0.00 6-10 Sat
## 6 1 27.0 6.70 5.33 6-10 Sat
## dropoff_hour dropoff_dow pickup_nhood dropoff_nhood kSplits
## 1 6-10 Thu Garment District Murray Hill D
## 2 6-10 Sat Soho Clinton G
## 3 6-10 Sat Morningside Heights Hamilton Heights A
## 4 6-10 Sat Tribeca Tribeca B
## 5 6-10 Sat Midtown Chelsea F
## 6 6-10 Sat <NA> Midtown I
class(taxi_xdf)
## [1] "RxXdfData"
## attr(,"package")
## [1] "RevoScaleR"
dplyrXdf package can also be completed by using the rxDataStep function in the RevoScaleR package included with your MRS installationdplyrXdf consists almost entirely of wrapper functions that call on other RevoScaleR functionsrxDataStep vs dplyrXdftaxi_xdf %>% rxGetInfo(getVarInfo = TRUE)
## File name: /datadrive/alizaidi/tripdata_2015.xdf
## Number of observations: 37696906
## Number of variables: 16
## Number of blocks: 21
## Compression type: zlib
## Variable information:
## Var 1: VendorID
## 2 factor levels: 2 1
## Var 2: passenger_count, Type: integer, Low/High: (1, 9)
## Var 3: trip_distance, Type: numeric, Low/High: (0.0000, 49.9000)
## Var 4: RateCodeID
## 7 factor levels: 1 2 5 3 4 99 6
## Var 5: store_and_fwd_flag
## 2 factor levels: N Y
## Var 6: payment_type
## 5 factor levels: 1 2 3 4 5
## Var 7: fare_amount, Type: numeric, Low/High: (0.0100, 503325.5300)
## Var 8: tip_amount, Type: numeric, Low/High: (-0.0100, 3950588.8000)
## Var 9: tolls_amount, Type: numeric, Low/High: (0.0000, 1000.6600)
## Var 10: pickup_hour
## 6 factor levels: 5-9 9-12 12-4 4-6 6-10 10-5
## Var 11: pickup_dow
## 7 factor levels: Sun Mon Tue Wed Thu Fri Sat
## Var 12: dropoff_hour
## 6 factor levels: 5-9 9-12 12-4 4-6 6-10 10-5
## Var 13: dropoff_dow
## 7 factor levels: Sun Mon Tue Wed Thu Fri Sat
## Var 14: pickup_nhood
## 263 factor levels: 19th Ward Abbott McKinley Albright Allen Annandale ... Woodhaven-Richmond Hill Woodlawn-Nordwood Woodrow Woodside Yorkville
## Var 15: dropoff_nhood
## 263 factor levels: 19th Ward Abbott McKinley Albright Allen Annandale ... Woodhaven-Richmond Hill Woodlawn-Nordwood Woodrow Woodside Yorkville
## Var 16: kSplits
## 10 factor levels: A B C D E F G H I J
taxi_transform <- RxXdfData(your_data)
system.time(taxi_transform <- rxDataStep(inData = taxi_xdf,
outFile = taxi_transform,
transforms = list(tip_pct = tip_amount/fare_amount),
overwrite = TRUE))
## user system elapsed
## 1.502 2.070 41.808
rxGetInfo(RxXdfData(taxi_transform), numRows = 2)
## File name: /datadrive/alizaidi/tripdata_2015.xdf
## Number of observations: 37696906
## Number of variables: 17
## Number of blocks: 21
## Compression type: zlib
## Data (2 rows starting with row 1):
## VendorID passenger_count trip_distance RateCodeID store_and_fwd_flag
## 1 2 1 1.59 1 N
## 2 1 1 3.30 1 N
## payment_type fare_amount tip_amount tolls_amount pickup_hour pickup_dow
## 1 1 12.0 3.25 0 6-10 Thu
## 2 1 14.5 2.00 0 6-10 Sat
## dropoff_hour dropoff_dow pickup_nhood dropoff_nhood kSplits
## 1 6-10 Thu Garment District Murray Hill D
## 2 6-10 Sat Soho Clinton G
## tip_pct
## 1 0.2708333
## 2 0.1379310
dplyrXdf, using the exact same syntax that we learned in the dplyr module and taking advantage of the %>% operatorsystem.time(taxi_transform <- taxi_xdf %>% mutate(tip_pct = tip_amount/fare_amount))
## user system elapsed
## 1.537 2.104 43.378
taxi_transform %>% rxGetInfo(numRows = 2)
## File name: /tmp/RtmphytGxr/file7373ff7d85e.xdf
## Number of observations: 37696906
## Number of variables: 17
## Number of blocks: 21
## Compression type: zlib
## Data (2 rows starting with row 1):
## VendorID passenger_count trip_distance RateCodeID store_and_fwd_flag
## 1 2 1 1.59 1 N
## 2 1 1 3.30 1 N
## payment_type fare_amount tip_amount tolls_amount pickup_hour pickup_dow
## 1 1 12.0 3.25 0 6-10 Thu
## 2 1 14.5 2.00 0 6-10 Sat
## dropoff_hour dropoff_dow pickup_nhood dropoff_nhood kSplits
## 1 6-10 Thu Garment District Murray Hill D
## 2 6-10 Sat Soho Clinton G
## tip_pct
## 1 0.2708333
## 2 0.1379310
rxDataStep operation and the dplyrXdf method, is that we do not specify an outFile argument anywhere in the dplyrXdf pipelinemutate value to a new variable called taxi_transformxdf, and only saves the most recent output of a pipeline, where a pipeline is defined as all operations starting from a raw xdf file.persist verbtaxi_transform@file
## [1] "/tmp/RtmphytGxr/file7373ff7d85e.xdf"
persist(taxi_transform, outFile = "taxiTransform.xdf") -> taxi_transform
dplyrXdf package really shines when used for data aggregations and summarizationsrxSummary, rxCube, and rxCrossTabs can compute a few summary statistics and do aggregations very quickly, they are not sufficiently general to be used in all placestaxi_group <- taxi_transform %>%
group_by(pickup_nhood) %>%
summarize(ave_tip_pct = mean(tip_pct))
taxi_group %>% head
## pickup_nhood ave_tip_pct
## 1 Annandale 0.13750000
## 2 Ardon Heights 0.03814815
## 3 Astoria-Long Island City 0.12307670
## 4 Auburndale 0.14224544
## 5 Battery Park 0.15798303
## 6 Bay Ridge 0.40164238
rxCube as well, but would require additional considerationspickup_nhood column was a factor (can’t mutate in place because of different data types)rxCube can only provide summations and averages, so we cannot get standard deviations for instance.rxFactors(inData = taxi_transform,
outFile = "/datadrive/alizaidi/taxi_factor.xdf",
factorInfo = c("pickup_nhood"),
overwrite = TRUE)
## Warning in factorInfoVarList(factorInfo[i], varInfo, sortLevelsDefault = sortLevels, :
## No changes will be made to the factor variable 'pickup_nhood'
## because 'sortLevels = FALSE' and there is no 'indexMap'.
## Warning in rxFactorsBase(inData = dataIO[["inData"]], factorInfo =
## factorInfo, : No changes made to the data set.
head(rxCube(tip_pct ~ pickup_nhood,
means = TRUE,
data = "/datadrive/alizaidi/taxi_factor.xdf"))
## pickup_nhood tip_pct Counts
## 1 19th Ward NaN 0
## 2 Abbott McKinley NaN 0
## 3 Albright NaN 0
## 4 Allen NaN 0
## 5 Annandale 0.1375 2
## 6 Arbor Hill NaN 0
# file.remove("data/taxi_factor.xdf")
As we saw above, it’s pretty easy to create a summarization or aggregation script. We can encapsulate our aggregation into it’s own function. Suppose we wanted to calculate average tip as a function of dropoff and pickup neighborhoods. In the dplyr nonmenclature, this means grouping by dropoff and pickup neighborhoods, and summarizing/averaging tip percent.
rxGetInfo(taxi_transform, numRows = 5)
## File name: /home/alizaidi/mr4ds/Student-Resources/rmarkdown/taxiTransform.xdf
## Number of observations: 37696906
## Number of variables: 17
## Number of blocks: 76
## Compression type: zlib
## Data (5 rows starting with row 1):
## VendorID passenger_count trip_distance RateCodeID store_and_fwd_flag
## 1 2 1 1.59 1 N
## 2 1 1 3.30 1 N
## 3 1 1 1.80 1 N
## 4 1 1 0.50 1 N
## 5 1 1 3.00 1 N
## payment_type fare_amount tip_amount tolls_amount pickup_hour pickup_dow
## 1 1 12.0 3.25 0 6-10 Thu
## 2 1 14.5 2.00 0 6-10 Sat
## 3 2 9.5 0.00 0 6-10 Sat
## 4 2 3.5 0.00 0 6-10 Sat
## 5 2 15.0 0.00 0 6-10 Sat
## dropoff_hour dropoff_dow pickup_nhood dropoff_nhood kSplits
## 1 6-10 Thu Garment District Murray Hill D
## 2 6-10 Sat Soho Clinton G
## 3 6-10 Sat Morningside Heights Hamilton Heights A
## 4 6-10 Sat Tribeca Tribeca B
## 5 6-10 Sat Midtown Chelsea F
## tip_pct
## 1 0.2708333
## 2 0.1379310
## 3 0.0000000
## 4 0.0000000
## 5 0.0000000
mht_url <- "http://alizaidi.blob.core.windows.net/training/manhattan.rds"
manhattan_hoods <- readRDS(gzcon(url(mht_url)))
taxi_transform %>%
filter(pickup_nhood %in% mht_hoods,
dropoff_nhood %in% mht_hoods,
.rxArgs = list(transformObjects = list(mht_hoods = manhattan_hoods))) %>%
group_by(dropoff_nhood, pickup_nhood) %>%
summarize(ave_tip = mean(tip_pct),
ave_dist = mean(trip_distance)) %>%
filter(ave_dist > 3, ave_tip > 0.05) -> sum_df
sum_df %>% rxGetInfo(getVarInfo = TRUE, numRows = 5)
## File name: /tmp/RtmphytGxr/file73737ad66f7d.xdf
## Number of observations: 408
## Number of variables: 4
## Number of blocks: 1
## Compression type: zlib
## Variable information:
## Var 1: dropoff_nhood
## 263 factor levels: 19th Ward Abbott McKinley Albright Allen Annandale ... Woodhaven-Richmond Hill Woodlawn-Nordwood Woodrow Woodside Yorkville
## Var 2: pickup_nhood
## 263 factor levels: 19th Ward Abbott McKinley Albright Allen Annandale ... Woodhaven-Richmond Hill Woodlawn-Nordwood Woodrow Woodside Yorkville
## Var 3: ave_tip, Type: numeric, Low/High: (0.0579, 0.1876)
## Var 4: ave_dist, Type: numeric, Low/High: (3.0004, 13.9100)
## Data (5 rows starting with row 1):
## dropoff_nhood pickup_nhood ave_tip ave_dist
## 1 Central Park Battery Park 0.1160478 6.028228
## 2 Clinton Battery Park 0.1300344 3.878010
## 3 East Harlem Battery Park 0.1102473 9.982296
## 4 East Village Battery Park 0.1401632 3.952964
## 5 Garment District Battery Park 0.1312443 3.874708
class(sum_df)
## [1] "grouped_tbl_xdf"
## attr(,"package")
## [1] "dplyrXdf"
Alternatively, we can encapsulate this script into a function, so that we can easily call it in a functional pipeline.
taxi_hood_sum <- function(taxi_data = taxi_df, ...) {
taxi_data %>%
filter(pickup_nhood %in% manhattan_hoods,
dropoff_nhood %in% manhattan_hoods, ...) %>%
group_by(dropoff_nhood, pickup_nhood) %>%
summarize(ave_tip = mean(tip_pct),
ave_dist = mean(trip_distance)) %>%
filter(ave_dist > 3, ave_tip > 0.05) -> sum_df
return(sum_df)
}
The resulting summary object isn’t very large (about 408 rows in this case), so it shouldn’t cause any memory overhead issues if we covert it now to a data.frame. We can plot our results using our favorite plotting library.
tile_plot_hood <- function(df = taxi_hood_sum()) {
library(ggplot2)
ggplot(data = df, aes(x = pickup_nhood, y = dropoff_nhood)) +
geom_tile(aes(fill = ave_tip), colour = "white") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = 'bottom') +
scale_fill_gradient(low = "white", high = "steelblue") -> gplot
return(gplot)
}
# tile_plot_hood(as.data.frame(sum_df))
taxi_transform <- taxi_xdf %>% mutate(tip_pct = tip_amount/fare_amount)
library(plotly)
sum_df <- taxi_hood_sum(taxi_transform,
.rxArgs = list(transformObjects = list(manhattan_hoods = manhattan_hoods))) %>%
persist("/datadrive/alizaidi/summarized.xdf")
ggplotly(tile_plot_hood(as.data.frame(sum_df)))
The do verb is an exception to the rule that dplyrXdf verbs write their output as xdf files. This is because do executes arbitrary R code, and can return arbitrary R objects; while a data frame is capable of storing these objects, an xdf file is limited to character and numeric vectors only.
The doXdf verb is similar to do, but where do splits its input into one data frame per group, doXdf splits it into one xdf file per group. This allows do-like functionality with grouped data, where each group can be arbitrarily large. The syntax for the two functions is essentially the same, although the code passed to doXdf must obviously know how to handle xdfs.
taxi_models <- taxi_xdf %>% group_by(pickup_dow) %>% doXdf(model = rxLinMod(tip_amount ~ fare_amount, data = .))
taxi_models
## Source: local data frame [7 x 2]
## Groups: <by row>
##
## # A tibble: 7 × 2
## pickup_dow model
## * <fctr> <list>
## 1 Fri <S3: rxLinMod>
## 2 Mon <S3: rxLinMod>
## 3 Sat <S3: rxLinMod>
## 4 Sun <S3: rxLinMod>
## 5 Thu <S3: rxLinMod>
## 6 Tue <S3: rxLinMod>
## 7 Wed <S3: rxLinMod>
taxi_models$model[[1]]
## Call:
## rxLinMod(formula = tip_amount ~ fare_amount, data = .)
##
## Linear Regression Results for: tip_amount ~ fare_amount
## Data: . (RxXdfData Data Source)
## File name: /tmp/RtmphytGxr/file73731f59af6.pickup_dow.Fri.xdf
## Dependent variable(s): tip_amount
## Total independent variables: 2
## Number of valid observations: 5885510
## Number of missing observations: 0
##
## Coefficients:
## tip_amount
## (Intercept) 1.6567742764
## fare_amount 0.0007725297
All the caveats that go with working with data.frames apply here. While each grouped partition is it’s own RxXdfData object, the return value must be a data.frame, and hence, must fit in memory. Moreover, the function you apply against the splits will determine how they are operated. If you use an rx function, you’ll get the nice fault-tolerant, parallel execution strategies the RevoScaleR package provides, but for any vanilla/CRAN function will work with data.frames and can easily cause your session to crash.
library(broom)
taxi_broom <- taxi_xdf %>% group_by(pickup_dow) %>% doXdf(model = lm(tip_amount ~ fare_amount, data = .))
Now we can apply the broom::tidy function at the row level to get summary statistics:
library(broom)
tbl_df(taxi_broom) %>% tidy(model)
## Source: local data frame [14 x 6]
## Groups: pickup_dow [7]
##
## pickup_dow term estimate std.error statistic
## <fctr> <chr> <dbl> <dbl> <dbl>
## 1 Sun (Intercept) 2.3135094526 7.571328e-01 3.0556190
## 2 Sun fare_amount 0.0004142143 3.428908e-03 0.1208006
## 3 Mon (Intercept) 0.0874830714 1.422496e-03 61.4996928
## 4 Mon fare_amount 0.1273401404 8.615129e-05 1478.0991027
## 5 Tue (Intercept) 1.6038100602 1.087692e-03 1474.5078486
## 6 Tue fare_amount 0.0071964814 2.400197e-05 299.8287270
## 7 Wed (Intercept) 0.0115389375 1.377781e-03 8.3750150
## 8 Wed fare_amount 0.1386170412 8.666624e-05 1599.4352913
## 9 Thu (Intercept) 0.2319764306 1.340523e-03 173.0491740
## 10 Thu fare_amount 0.1180815950 8.138576e-05 1450.8876936
## 11 Fri (Intercept) 1.6567742764 1.035926e-03 1599.3176836
## 12 Fri fare_amount 0.0007725297 7.730892e-06 99.9276322
## 13 Sat (Intercept) 1.4252806941 8.801076e-04 1619.4390909
## 14 Sat fare_amount 0.0025861524 1.405561e-05 183.9943833
## # ... with 1 more variables: p.value <dbl>